India’s approach to artificial intelligence is increasingly shaped by a normative idea rather than a narrow technological race: the democratisation of AI. The central premise is that AI should not remain concentrated in a few global firms or elite institutions, but should function as a public-good–oriented technology that improves welfare, productivity and governance for all citizens. This vision — often framed as “AI for Humanity” — places people, not algorithms, at the centre of innovation.
For this vision to translate into real-world outcomes, AI must operate reliably at population scale and integrate seamlessly into everyday services such as healthcare, education, agriculture, finance and public administration. Achieving this scale is not possible through standalone applications. It requires a strong, integrated AI stack — a layered ecosystem of applications, models, compute, infrastructure and energy that together enable AI to be built, deployed and sustained at scale.
What is an AI stack and why does it matter?
An AI stack refers to the complete set of technologies and systems that work together to make artificial intelligence usable in the real world. From data collection and model training to deployment and everyday use, the AI stack ensures that intelligence flows smoothly from back-end infrastructure to front-end citizen services.
Much like digital public infrastructure enabled population-scale adoption of Aadhaar, UPI and mobile internet, a robust AI stack determines whether AI remains experimental or becomes transformational. In India’s context, the AI stack is designed not just for efficiency, but for inclusion, affordability and sovereignty.
The application layer: where citizens encounter AI
The application layer is the most visible part of the AI stack. It includes AI-powered tools and services that people directly interact with — health diagnostics, crop advisory platforms, language translation tools, chatbots, learning platforms and judicial services.
In India, AI applications are increasingly tailored to local languages, socio-economic contexts and sector-specific challenges. In agriculture, AI-based advisories are improving sowing decisions, pest management and input efficiency, with state-level deployments in Andhra Pradesh and Maharashtra reporting productivity gains of 30–50%. In healthcare, AI tools support early detection of tuberculosis, cancer and neurological disorders, strengthening preventive care in resource-constrained settings.
In education, the National Education Policy (NEP) 2020 integrates AI exposure through CBSE curricula and platforms like DIKSHA, while initiatives such as YUVAi aim to equip students with applied AI skills. In justice delivery, e-Courts Phase III uses AI and machine learning for translation, scheduling and case management, improving transparency and access in regional languages. In weather and disaster management, the India Meteorological Department deploys AI for advanced forecasting of cyclones, rainfall, fog and lightning, supporting farmers and emergency response systems.
The application layer ultimately determines AI’s social value. When adopted at scale across priority sectors, it allows AI to move beyond pilots and become embedded in everyday governance and service delivery.
The AI model layer: building India-centric intelligence
If applications are the interface, AI models form the cognitive core of the system. These models learn from data to recognise patterns, make predictions and support decisions — from medical image analysis and language translation to crop yield forecasting and conversational systems.
India is actively developing sovereign and India-centric AI models to reduce dependence on external ecosystems. Under the IndiaAI Mission, 12 indigenous AI models are being developed for national priorities. BharatGen is working on large, multimodal foundation models suited to Indian languages and contexts, while IndiaAIKosh serves as a national repository of datasets, models and tools, hosting thousands of datasets across sectors.
Startups such as Sarvam AI are building large language and speech models for Indian languages, enabling voice-based interfaces and citizen services. Bhashini, under the National Language Translation Mission, hosts hundreds of models for speech recognition, translation and text-to-speech, strengthening multilingual digital access.
This emphasis on sovereign models ensures relevance, trust and cultural alignment, while supporting scalable innovation without strategic dependence on foreign platforms.
The compute layer: removing the biggest barrier to AI
AI systems require immense computing power to train and operate models. This compute layer — powered by GPUs, TPUs and specialised accelerators — determines the speed, scale and sophistication of AI innovation.
Historically, access to high-end compute has been limited by cost and concentration among a few global firms. India is addressing this constraint through shared public infrastructure. Under the IndiaAI Mission, over ₹10,300 crore has been allocated to provide compute-as-a-service through the IndiaAI Compute Portal, offering subsidised access to tens of thousands of GPUs and TPUs.
Alongside this, India is strengthening domestic hardware capabilities through the India Semiconductor Mission, indigenous chip initiatives such as SHAKTI and VEGA, and the National Supercomputing Mission. Flagship systems like PARAM Siddhi-AI and AIRAWAT support advanced applications in language processing, climate modelling and drug discovery.
By lowering entry barriers, the compute layer enables startups, researchers and public institutions to participate meaningfully in AI development.
Data centres and networks: the backbone of scale
AI cannot function without reliable digital infrastructure. Data centres store and run AI systems, while broadband, fibre and 5G networks move data between users and machines in real time.
India has rapidly expanded this backbone. Nationwide optical fibre connectivity and near-universal 5G coverage enable low-latency AI services. Data centre capacity, currently under 1 GW, is projected to expand sharply by 2030, driven by AI and cloud demand. Mumbai–Navi Mumbai has emerged as the largest hub, with other centres spread across Bengaluru, Hyderabad, Chennai and Delhi NCR.
Major global investments by firms such as Microsoft, Amazon and Google underscore India’s emergence as a key AI infrastructure destination. Hosting data and models domestically also supports data sovereignty and regulatory control.
The energy layer: powering AI sustainably
AI infrastructure is energy-intensive. Data centres and high-performance computing facilities require uninterrupted, affordable electricity. Without energy security, AI ambitions cannot scale.
India’s power sector transformation underpins this layer. Installed capacity has crossed 500 GW, with over half coming from non-fossil fuel sources. Investments in pumped storage, battery energy storage and nuclear energy — including policy support for small modular reactors — aim to ensure grid stability alongside renewable expansion.
This shift allows AI growth to align with climate goals, reducing the carbon footprint of digital infrastructure while maintaining reliability.
Why the integrated stack matters for India
Each layer of the AI stack is necessary but insufficient on its own. Applications need relevant models; models need compute; compute needs infrastructure; infrastructure needs energy. India’s strategy recognises this interdependence and focuses on strengthening all layers simultaneously.
By doing so, India is pursuing an “AI diffusion” model — prioritising widespread adoption over isolated breakthroughs. This approach mirrors earlier successes in digital public infrastructure and positions AI as an enabler of inclusive growth rather than a source of inequality.
What to note for Prelims?
- AI stack consists of applications, models, compute, infrastructure and energy.
- IndiaAI Mission supports indigenous AI models and shared compute access.
- Bhashini and BharatGen focus on Indian languages and contexts.
- Energy and data centres are critical enablers of AI at scale.
What to note for Mains?
- Explain how an integrated AI stack enables population-scale AI adoption.
- Discuss AI democratisation as a development strategy for India.
- Role of public infrastructure in reducing AI entry barriers.
- Link between AI, energy transition and digital sovereignty.
